AI Fundamentals: How Machines Learn

AI leaders need a solid grasp of the fundamentals. Before you can lead with AI — whether in business, education, or creative work — you need to understand what it actually means for a computer to “learn.”

This workshop is the first in a new series on AI Fundamentals, designed for absolute beginners who want to build a foundation of knowledge that supports confident, informed leadership. The sessions accompany ideas introduced in AI Essentials for Leaders (De Gruyter, 2025), providing a guided and highly accessible path through the core concepts every aspiring AI leader should know.

We’ll start where all AI understanding begins: the shift from traditional programming (where humans write explicit rules) to machine learning (where computers learn patterns directly from data). You’ll also explore one of the most fundamental tools in machine learning — supervised learning, and how it powers real-world predictions, from credit scoring to price forecasting.

No coding experience is required. Just curiosity and a desire to think like an AI leader.

## What You’ll Learn

You’ll develop a clear, intuitive understanding of:

• How traditional programming and machine learning differ
• Why “learning from data” represents a paradigm shift in computing
• How supervised learning enables prediction and decision-making
• What concepts like “training,” “features,” and “models” actually mean in plain English

## Workshop Stages

### Stage 1: How Traditional Programming Works

We begin with how computers have operated for decades: they follow explicit instructions written by humans. You’ll see how algorithms transform clear rules into predictable output and why this approach fails when problems become too complex or ambiguous.

Key Learning: Traditional programming relies on explicit human instruction.

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### Stage 2: How Machines Learn

Next, we’ll look at how machine learning reverses the process. Instead of programmers defining every rule, we supply the computer with examples and correct answers so it can infer the rules itself. You’ll see, step by step, how a system builds a model from patterns hidden in data.
Key Learning: Machine learning extracts rules and instructions from data.

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### Stage 3: Learning by Example — Predicting with Regression

We’ll walk through a conceptual case study: predicting house prices using Multiple Linear Regression. This exercise shows how a simple model can connect several factors — like size, location, and condition — to generate useful predictions.
Key Learning: Even basic models can reveal powerful patterns when grounded in data.

## What You’ll Take Away

• A foundational understanding of the shift from programming to learning
• The ability to explain supervised machine learning in plain terms
• A practical framework for evaluating where AI fits into your work or organization
• A roadmap for future workshops in the AI Fundamentals series

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